Fixed-time stabilization of discontinuous spatiotemporal neural networks with time-varying coefficients via aperiodically switching control

被引:0
作者
Xiaofang HU [1 ,2 ,3 ]
Leimin WANG [1 ,2 ,3 ]
ChuanKe ZHANG [1 ,2 ,3 ]
Xiongbo WAN [1 ,2 ,3 ]
Yong HE [1 ,2 ,3 ]
机构
[1] School of Automation, China University of Geosciences
[2] Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems
[3] Engineering Research Center of Intelligent Technology for Geo-Exploration,Ministry of Education
关键词
D O I
暂无
中图分类号
TP183 [人工神经网络与计算]; TP273 [自动控制、自动控制系统];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ; 080201 ;
摘要
This paper focuses on the challenge of fixed-time control for spatiotemporal neural networks(SNNs) with discontinuous activations and time-varying coefficients. A novel fixed-time convergence lemma is proposed, which facilitates the handling of time-varying coefficients of SNNs and relaxes the restriction on the non-positive definiteness of the derivative of the Lyapunov function. Besides, a more flexible and economical aperiodically switching control technique is presented to stabilize SNNs within a fixed time,efectively reducing the amount of information transmission and control costs. Under the newly established fixed-time convergence lemma and aperiodically switching controller, many more general algebraic conditions are deduced to ensure the fixed-time stabilization of SNNs. Numerical examples are provided to manifest the validity of the results.
引用
收藏
页码:182 / 195
页数:14
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